Entrepreneurial organizing activities and nascent venture performance

This research provides an improved understanding of how ventures successfully organize via resource allocations. Conceptually, we apply elements of action theory to account for resource trade-offs that occur as entrepreneurs make decisions about adding staff members to boundary spanning, technical core, and management functions. We then model how these allocation decisions differentially impact nascent venture performance. Empirically, we test our model with a sample of 2484 entrepreneurs captured in the Kauffman Firm Survey, a longitudinal dataset that tracks a random sample of US startups over an 8-year period. Results from dynamic panel estimation reveal evidence of both performance penalties and performance boosts as the result of entrepreneurs adding staff to specific areas, revealing optimality in specific configurations of entrepreneurial organizing elements.

Understanding Preferences in Tourism Email Marketing

Nowadays, advanced tools allow the personalization of email communication with tourism clients or prospects based on explicit (e.g. name, age, language, country) and implicit indicators (e.g. ranking of activity in the opening rate of the newsletter, browsing preferences, online preferences provided by cookies, etc.). However, knowing how audiences react to emails allows Destination Marketing Organizations (DMOs) to create content clusters for personalized communication. The purpose of this study is to empirically investigate the preferences on tourism email marketing of different audiences based on a specific explicit indicator, namely the language chosen by users to receive communications by a DMO. A content analysis on a longitudinal dataset based on 131 newsletter messages sent between 2018 and 2021 to more than 50′000 contacts by a DMO in Switzerland was performed. Results show that content should be adapted to different audiences speaking different languages instead of providing just a translation. Specifically, the German-speaking audience seems to be more inclined to messages that focus on winter sports and hiking, the Italian-speaking audience to news about hiking and information on COVID-19, the French-speaking audience to news about promotions, while the English-speaking audience to contents on discounts and COVID-19-related. These results provide an important contribution to studies on tourism personalization of communication in the context of email marketing, suggesting the role of content adaptation according to the language and cultural background of the audience. DMO managers can also benefit from this research in understanding how to address a similar study on their datasets and compare the emerged content clusters.

Does Regional Political Representation affect the Provision of Childcare in Austria?

Historically the provision of childcare was one of the forgotten areas of Austrian family policy. During the last decade, a lot of effort was made to catch up with other European countries, but notable subnational differences between the regions and municipalities persist. This paper engages with the arising question: Does regional political representation affect the provision of childcare in Austria? Based on a unique longitudinal dataset (2003-2018) containing yearly measures for 1789 Austrian municipalities, several hierarchical regression models are calculated. The results reveal a significant effect of gender and partisan dimension, as well as an interaction effect between those dimensions.

CHRO firm dinosaur versus CHRO role gorilla: the effect of CHRO company and role tenure on firms’ social performance

Academics and practitioners emphasize the rising importance of Chief Human Resource Officers (CHROs). CHROs act as heads of staff—they motivate the personnel and offer guidance. This study helps clarify the impact of increasingly relevant CHROs and reveals how their company and role tenure influence firms’ social performance. Drawing on a multisource longitudinal dataset of S&P 500 firms, we empirically validate our hypotheses. The sample contains 283 companies with 1944 firm-year observations from 2005 to 2017 and combines manually collected top management team data with data from Thomson Reuters Datastream. Our results show that there is a negative relation between CHROs with long company tenure and firms’ social performance, whereas CHROs with long role tenure positively relate to firms’ social performance. We also investigate the moderating role of CEO prior experience (i.e., HR experience, education, company and role experience) on the effect of CHRO company and role tenure on firms’ social performance. Surprisingly, CEO prior experience negatively moderates the relationship between CHRO role tenure and firms’ social performance. Overall, this article offers novel implications for the CHRO role and uncovers a distinction between two types of CHROs: CHRO firm dinosaurs versus CHRO role gorillas.

More alike than different: Novel methods for measuring and modeling executive function development

Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented rigorous understanding of the precise ways EFs are organized in childhood and how they develop over time. We introduce novel methods to address these challenges for both measuring and modeling EFs using a large, accelerated longitudinal dataset from a diverse sample of students in middle childhood (approximately ages 8 to 14; N = 1,286). Adaptive assessments allowed us to equate EF challenge across ages and a data-driven, network analytic approach revealed the evolving diversity of EFs while accounting for their unity. Our results suggest EF organization stabilizes around age 10, but continues refining through at least age 14. This approach brings new precision to EFs’ development by removing interpretative ambiguities associated with previous methodologies. By improving EF measurement, the field can move towards improving EF training, to provide a strong foundation for students’ success.

EXPRESS: Demand Interactions in Sharing Economies: Evidence from a Natural Experiment Involving Airbnb and Uber/Lyft

We examine whether and how ride-sharing services influence the demand for home-sharing services. Our identification strategy hinges on a natural experiment in which Uber/Lyft exited Austin, Texas, in May 2016 due to local regulation. Using a 12-month longitudinal dataset of 11,536 Airbnb properties, we find that Uber/Lyft’s exit led to a 14% decrease in Airbnb occupancy in Austin. In response, hosts decreased the nightly rate by $9.3 and the supply by 4.5%. We argue that when Uber/Lyft exited Austin, the transportation costs for most Airbnb guests increased significantly because most Airbnb properties (unlike hotels) have poor access to public transportation. We report three key findings: First, demand became less geographically dispersed, falling (increasing) for Airbnb properties with poor (excellent) access to public transportation. Second, demand decreased significantly for low-end properties, whose guests may be more price-sensitive, but not for high-end properties. Third, the occupancy of Austin hotels increased after Uber/Lyft’s exit; the increase occurred primarily among low-end hotels, which can substitute for low-end Airbnb properties. The results indicate that access to affordable, convenient transportation is critical for the success of home-sharing services in residential areas. Regulations that negatively affect ride-sharing services may also negatively affect the demand for home-sharing services.

Increased Generosity Under COVID-19 Threat

In the face of crises – wars, pandemics, and natural disasters – both increased selfishness and increased generosity may emerge. In this paper, we study the relationship between the presence of COVID-19 threat and generosity using a four-year longitudinal dataset (N = 696,942) capturing real donations made before and during the pandemic, as well as six-months dictator game allocations (N = 1,003 participants) made during the early months of the pandemic. Consistent with the notion of “catastrophe compassion” (Zaki 2020), and contrary to prior research showing a tendency toward self-interested behavior under threat, individuals across both datasets exhibited greater financial generosity when their county experienced COVID-19 threat. While we find that the presence of threat impacted individual giving, behavior was not sensitive to threat level. Our findings have significant societal implications and advance our understanding of economic and psychological theories of social preferences under threat.

Do smartphone application preferences change over time or vary by demographics? A longitudinal study using behavioral data

Smartphones afford users the ability to select their own custom mobile application repertoires through the installation of a nearly endless array of applications. Acknowledging the need for increased attention to the description of digital media usage, this paper reports a quantitative descriptive study that investigates the types of applications that people commonly use, the amount of time they spend with these applications, the application combinations that they construct, the consistency of these combinations over time, and differences in these outcomes by three demographic characteristics. Using a longitudinal dataset that includes behavioural data collected via data donations, the study identifies key application adoption patterns and shows that peoples’ mobile application repertoires are concentrated around a subset of popular applications that is relatively consistent over time. However, within this subset there is substantial diversity between applications and between individuals. These results suggest that quantifying smartphone usage with a single metric—total aggregate usage duration (i.e., screentime)—is unlikely to capture the full extent and diversity of media that users curate for themselves as part of their mobile application repertoires.

More alike than different: Novel methods for measuring and modeling executive function development

Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented rigorous understanding of the precise ways EFs are organized in childhood and how they develop over time. We introduce novel methods to address these challenges for both measuring and modeling EFs using a large, accelerated longitudinal dataset from a diverse sample of students in middle childhood (approximately ages 8 to 14; N = 1,286). Adaptive assessments allowed us to equate EF challenge across ages and a data-driven, network analytic approach revealed the evolving diversity of EFs while accounting for their unity. Our results suggest EF organization stabilizes around age 10, but continues refining through at least age 14. This approach brings new precision to EFs’ development by removing interpretative ambiguities associated with previous methodologies. By improving EF measurement, the field can move towards improving EF training, to provide a strong foundation for students’ success.